h dZddlZddlmZmZddlmZmZddlZ ddl m Z ddl m Z mZmZmZmZmZddlmZdd lmZmZdd lmZmZdd lmZdd lmZmZdd l m!Z!m"Z"gdZ#eedkrddl m$Z%nddl m%Z%dZ& d'dZ'dZ(d(dZ)dZ*Gddeeeee eZ+Gdd e+Z,Gd!d"e+Z-Gd#d$e+Z.Gd%d&eee Z/dS))zG The :mod:`sklearn.pls` module implements Partial Least Squares (PLS). N)ABCMetaabstractmethod)IntegralReal)svd) BaseEstimatorClassNamePrefixFeaturesOutMixinMultiOutputMixinRegressorMixinTransformerMixin _fit_context)ConvergenceWarning) check_arraycheck_consistent_length)Interval StrOptions)svd_flip) parse_version sp_version) FLOAT_DTYPEScheck_is_fitted) PLSCanonical PLSRegressionPLSSVDz1.7)pinv)pinv2c t|dd\}}}|jj}ddd}t j|||zt j|jz}t j||k}|ddd|f}||d|z}t j t j t j ||d|S)NF) full_matrices check_finiteg@@g.A)fd) rdtypecharlowernpmaxfinfoepssum transpose conjugatedot)ausvhtfactorcondranks h/var/www/html/services/rag-service/venv/lib/python3.11/site-packages/sklearn/cross_decomposition/_pls.py _pinv2_oldr7)s 1E>>>HAq"  AS ! !F 6!99vay 28A;;? 2D 6!d(  D !!!UdU( A5D5MA < RVAr%4%y%9%9:: ; ;;Aư>Fcntj|jj t fd|jD}n"#t $r}t d|d}~wwxYwd}|dkrt|t|} } t|D]r} |dkrtj | |} n0tj |j|tj ||z } | tj tj | | zz} tj || } |dkrtj | | }n5tj |j| tj | j| z }|r-|tj tj ||zz}tj ||tj ||zz }| |z }tj |||ks|j ddkrn| }t| dz}||krtj dt| ||fS)a?Return the first left and right singular vectors of X'Y. Provides an alternative to the svd(X'Y) and uses the power method instead. With norm_y_weights to True and in mode A, this corresponds to the algorithm section 11.3 of the Wegelin's review, except this starts at the "update saliences" part. c3pK|]0}tjtj|k,|V1dSN)r&anyabs).0colr)s r6 z;_get_first_singular_vectors_power_method..Hs?GGsRVBF3KK#4E-F-FGsGGGGGGr8Y residual is constantNdBz$Maximum number of iterations reached)r&r(r#r)nextT StopIterationr7ranger-sqrtshapewarningswarnr)XYmodemax_itertolnorm_y_weightsy_scoree x_weights_oldX_pinvY_pinvi x_weightsx_score y_weightsx_weights_diffn_iterr)s @r6(_get_first_singular_vectors_power_methodra;s- (17   C=GGGGacGGGGG ===4551<=M s{{$A 1  8__"" 3;;vw//IIqsG,,rvgw/G/GGIRWRVIy99::S@@ &I&& 3;;vw//IIqsG,,rvgi/I/III  E  9!=!=>>D DI&I&&"&I*F*F*LM"]2 6.. 1 1C 7 7171:?? E! UF  <>PQQQ i ''s A A! AA!ctj|j|}t|d\}}}|dddf|dddffS)zbReturn the first left and right singular vectors of X'Y. Here the whole SVD is computed. FrNr)r&r-rIr)rPrQCU_Vts r6_get_first_singular_vectors_svdrhvsP qsAA1E***HAq" QQQT7Bq!!!tH r8Tc|d}||z}|d}||z}|rK|dd}d||dk<||z}|dd}d||dk<||z}n>tj|jd}tj|jd}||||||fS)z{Center X, Y and scale if the scale parameter==True Returns ------- X, Y, x_mean, y_mean, x_std, y_std raxisrG)rkddofg?)meanstdr&onesrM)rPrQscalex_meany_meanx_stdy_stds r6_center_scale_xyrvsVVV^^FKA VVV^^FKA $11%%!esl U 11%%!esl U  ## ## a --r8ctjtj|}tj||}||z}||z}dS)z7Same as svd_flip but works on 1d arrays, and is inplaceN)r&argmaxr@sign)r/vbiggest_abs_val_idxrys r6 _svd_flip_1dr|sG)BF1II.. 71() * *DIAIAAAr8c PeZdZUdZeedddgdgeddhged d hged d hgeedddgeed ddgdgdZe e d<e dddd d dddddZ e ddZddZddZd dZddZdZdS)!_PLSaPartial Least Squares (PLS) This class implements the generic PLS algorithm. Main ref: Wegelin, a survey of Partial Least Squares (PLS) methods, with emphasis on the two-block case https://stat.uw.edu/sites/default/files/files/reports/2000/tr371.pdf rGNleftclosedboolean regression canonicalr9rFrnipalsr n_componentsrqdeflation_moderR algorithmrSrTcopy_parameter_constraintsrTr:r;)rqrrRrrSrTrcv||_||_||_||_||_||_||_||_dSr>)rrrRrqrrSrTr) selfrrqrrRrrSrTrs r6__init__z _PLS.__init__sB),  "   r8prefer_skip_nested_validationc $ t||||tj|jd}t |dtj|jd}|jdkrd|_|dd}nd|_|j d }|j d}|j d}|j }|j d kr|nt|||}||krtd |d |d |j dk|_|j}t|||j\} } |_|_|_|_tj||f|_tj||f|_tj||f|_tj||f|_tj||f|_tj||f|_g|_tj| jj} tA|D]w} |j!dkrtj"tj#| d| zkd } d| dd| f< tI| | |j%|j&|j'|\}}}nD#tP$r7}tS|dkrtUj+d| Yd}~nd}~wwxYw|j,|n|j!dkrt[| | \}}t]||tj/| |}|rd}ntj/||}tj/| ||z }tj/|| tj/||z }| tj0||z} |j dkrCtj/|| tj/||z }| tj0||z} |j d krCtj/|| tj/||z }| tj0||z} ||jdd| f<||jdd| f<||jdd| f<||jdd| f<||jdd| f<||jdd| f<ytj/|jtctj/|jj2|jd|_3tj/|jtctj/|jj2|jd|_4tj/|j3|jj2|_5|j5|jzj2|_5|j|_6|j3j d|_7|S)Fit model to data. Parameters ---------- X : array-like of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of predictors. Y : array-like of shape (n_samples,) or (n_samples, n_targets) Target vectors, where `n_samples` is the number of samples and `n_targets` is the number of response variables. Returns ------- self : object Fitted model. rr#rensure_min_samplesrQF input_namer#r ensure_2drGTrr`n_components` upper bound is . Got instead. Reduce `n_components`.rr rjrmN)rRrSrTrUrDz$Y residual is constant at iteration r)r )8r_validate_datar&float64rrndim _predict_1dreshaperMrrmin ValueError_norm_y_weightsrvrq_x_mean_y_mean_x_std_y_stdzeros x_weights_ y_weights_ _x_scores _y_scores x_loadings_ y_loadings_n_iter_r(r#r)rKrallr@rarRrSrTrJstrrNrOappendrhr|r-outerrrI x_rotations_ y_rotations_coef_ intercept__n_features_out)rrPrQnpqrrank_upper_boundrUXkYkY_epskYk_maskr\r^rrWx_scoresy_ssy_scores x_loadings y_loadingss r6fitz_PLS.fits& 1%%%    RZdiA     #RZdi5    6Q;;#D  "a  AA$D  GAJ GAJ GAJ( !% 3| C C11QPQST * * *F1AFF#FFF  $2kA-HX q$*H H DB dlDK(A|#455(A|#4551l"3441l"3448Q $5668Q $566 ""&|$$= 0= 0A~))&b5j!8qAAA!$111g: A!Y!% H'5  !!%1vv!999M"L"L"LMMMEEEEE   ##G,,,,5(('Fr2'N'N$ 9 I . . .vb),,H 4vi33vb),,t3H"--x0J0JJJ "(8Z00 0B"k11VHb11BF8X4N4NN bhx444"l22VHb11BF8X4N4NN bhx444$-DOAAAqD !$-DOAAAqD !#+DN111a4 #+DN111a4 %/D QQQT "%/D QQQT " "F O "&)+T_==E R R R  F O "&)+T_==E R R R  VD-t/?/ABB j4;.1 ,#06q9 s(J K +J>>Kct||||td}||jz}||jz}t j||j}|lt|dd|t}|j dkr| dd}||j z}||j z}t j||j }||fS|S)a.Apply the dimension reduction. Parameters ---------- X : array-like of shape (n_samples, n_features) Samples to transform. Y : array-like of shape (n_samples, n_targets), default=None Target vectors. copy : bool, default=True Whether to copy `X` and `Y`, or perform in-place normalization. Returns ------- x_scores, y_scores : array-like or tuple of array-like Return `x_scores` if `Y` is not given, `(x_scores, y_scores)` otherwise. Frr#resetNrQ)rrrr#rGr)rrrrrr&r-rrrrrrr)rrPrQrrrs r6 transformz_PLS.transformms&    L  N N T\ T[6!T.// =cU\Av{{IIb!$$  A  Ava!233HX% %r8cXt|t|dt}tj||jj}||jz}||jz }|Nt|dt}tj||j j}||j z}||j z }||fS|S)aeTransform data back to its original space. Parameters ---------- X : array-like of shape (n_samples, n_components) New data, where `n_samples` is the number of samples and `n_components` is the number of pls components. Y : array-like of shape (n_samples, n_components) New target, where `n_samples` is the number of samples and `n_components` is the number of pls components. Returns ------- X_reconstructed : ndarray of shape (n_samples, n_features) Return the reconstructed `X` data. Y_reconstructed : ndarray of shape (n_samples, n_targets) Return the reconstructed `X` target. Only returned when `Y` is given. Notes ----- This transformation will only be exact if `n_components=n_features`. rP)rr#NrQ) rrrr&matmulrrIrrrrr)rrPrQX_reconstructedY_reconstructeds r6inverse_transformz_PLS.inverse_transforms2  c > > >)At'7'9::4;&4<' =A#\BBBA i4+;+=>>O t{ *O t| +O"O3 3r8ct||||td}||jz}||jz}||jjz|jz}|jr| n|S)aUPredict targets of given samples. Parameters ---------- X : array-like of shape (n_samples, n_features) Samples. copy : bool, default=True Whether to copy `X` and `Y`, or perform in-place normalization. Returns ------- y_pred : ndarray of shape (n_samples,) or (n_samples, n_targets) Returns predicted values. Notes ----- This call requires the estimation of a matrix of shape `(n_features, n_targets)`, which may be an issue in high dimensional space. Fr) rrrrrrrIrrravel)rrPrYpreds r6predictz _PLS.predictsu,    L  N N T\ T[DJL 4?2 $ 0;u{{}}}e;r8cV|||||S)aLearn and apply the dimension reduction on the train data. Parameters ---------- X : array-like of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of predictors. y : array-like of shape (n_samples, n_targets), default=None Target vectors, where `n_samples` is the number of samples and `n_targets` is the number of response variables. Returns ------- self : ndarray of shape (n_samples, n_components) Return `x_scores` if `Y` is not given, `(x_scores, y_scores)` otherwise. rrrrPys r6 fit_transformz_PLS.fit_transform&$xx1~~''1---r8c dddS)NTF) poor_score requires_y)rs r6 _more_tagsz_PLS._more_tagss"%888r8r)NTr>T)__name__ __module__ __qualname____doc__rrrrrdict__annotations__rrrrrrrrrrr8r6r~r~s"(AtFCCCD%:|[&ABBCS#J''( j%!2334Xh4???@q$v6667  $ $D   #   ^*\555TT65Tl%%%%N****X<<<<<....(99999r8r~) metaclassceZdZUdZiejZeed<dD]Ze e d dddddfd Z fd Z xZ S) ra PLS regression. PLSRegression is also known as PLS2 or PLS1, depending on the number of targets. Read more in the :ref:`User Guide `. .. versionadded:: 0.8 Parameters ---------- n_components : int, default=2 Number of components to keep. Should be in `[1, min(n_samples, n_features, n_targets)]`. scale : bool, default=True Whether to scale `X` and `Y`. max_iter : int, default=500 The maximum number of iterations of the power method when `algorithm='nipals'`. Ignored otherwise. tol : float, default=1e-06 The tolerance used as convergence criteria in the power method: the algorithm stops whenever the squared norm of `u_i - u_{i-1}` is less than `tol`, where `u` corresponds to the left singular vector. copy : bool, default=True Whether to copy `X` and `Y` in :term:`fit` before applying centering, and potentially scaling. If `False`, these operations will be done inplace, modifying both arrays. Attributes ---------- x_weights_ : ndarray of shape (n_features, n_components) The left singular vectors of the cross-covariance matrices of each iteration. y_weights_ : ndarray of shape (n_targets, n_components) The right singular vectors of the cross-covariance matrices of each iteration. x_loadings_ : ndarray of shape (n_features, n_components) The loadings of `X`. y_loadings_ : ndarray of shape (n_targets, n_components) The loadings of `Y`. x_scores_ : ndarray of shape (n_samples, n_components) The transformed training samples. y_scores_ : ndarray of shape (n_samples, n_components) The transformed training targets. x_rotations_ : ndarray of shape (n_features, n_components) The projection matrix used to transform `X`. y_rotations_ : ndarray of shape (n_features, n_components) The projection matrix used to transform `Y`. coef_ : ndarray of shape (n_target, n_features) The coefficients of the linear model such that `Y` is approximated as `Y = X @ coef_.T + intercept_`. intercept_ : ndarray of shape (n_targets,) The intercepts of the linear model such that `Y` is approximated as `Y = X @ coef_.T + intercept_`. .. versionadded:: 1.1 n_iter_ : list of shape (n_components,) Number of iterations of the power method, for each component. n_features_in_ : int Number of features seen during :term:`fit`. feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- PLSCanonical : Partial Least Squares transformer and regressor. Examples -------- >>> from sklearn.cross_decomposition import PLSRegression >>> X = [[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [2.,5.,4.]] >>> Y = [[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]] >>> pls2 = PLSRegression(n_components=2) >>> pls2.fit(X, Y) PLSRegression() >>> Y_pred = pls2.predict(X) rrrRrrTr:r;rqrSrTrc Zt||ddd|||dS)Nrr9rrsuperrrrrqrSrTr __class__s r6rzPLSRegression.__init__csH %'  r8c|t|||j|_|j|_|S)r)rrr x_scores_r y_scores_)rrPrQrs r6rzPLSRegression.fitqs4$  Aq r8r) rrrrr~rrrparampoprr __classcell__rs@r6rrs``D$Cd&A#BDBBB8**""5))))  '+cu4        r8rceZdZUdZiejZeed<dD]Ze e d dddddd fd Z xZ S) ra Partial Least Squares transformer and regressor. Read more in the :ref:`User Guide `. .. versionadded:: 0.8 Parameters ---------- n_components : int, default=2 Number of components to keep. Should be in `[1, min(n_samples, n_features, n_targets)]`. scale : bool, default=True Whether to scale `X` and `Y`. algorithm : {'nipals', 'svd'}, default='nipals' The algorithm used to estimate the first singular vectors of the cross-covariance matrix. 'nipals' uses the power method while 'svd' will compute the whole SVD. max_iter : int, default=500 The maximum number of iterations of the power method when `algorithm='nipals'`. Ignored otherwise. tol : float, default=1e-06 The tolerance used as convergence criteria in the power method: the algorithm stops whenever the squared norm of `u_i - u_{i-1}` is less than `tol`, where `u` corresponds to the left singular vector. copy : bool, default=True Whether to copy `X` and `Y` in fit before applying centering, and potentially scaling. If False, these operations will be done inplace, modifying both arrays. Attributes ---------- x_weights_ : ndarray of shape (n_features, n_components) The left singular vectors of the cross-covariance matrices of each iteration. y_weights_ : ndarray of shape (n_targets, n_components) The right singular vectors of the cross-covariance matrices of each iteration. x_loadings_ : ndarray of shape (n_features, n_components) The loadings of `X`. y_loadings_ : ndarray of shape (n_targets, n_components) The loadings of `Y`. x_rotations_ : ndarray of shape (n_features, n_components) The projection matrix used to transform `X`. y_rotations_ : ndarray of shape (n_features, n_components) The projection matrix used to transform `Y`. coef_ : ndarray of shape (n_targets, n_features) The coefficients of the linear model such that `Y` is approximated as `Y = X @ coef_.T + intercept_`. intercept_ : ndarray of shape (n_targets,) The intercepts of the linear model such that `Y` is approximated as `Y = X @ coef_.T + intercept_`. .. versionadded:: 1.1 n_iter_ : list of shape (n_components,) Number of iterations of the power method, for each component. Empty if `algorithm='svd'`. n_features_in_ : int Number of features seen during :term:`fit`. feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- CCA : Canonical Correlation Analysis. PLSSVD : Partial Least Square SVD. Examples -------- >>> from sklearn.cross_decomposition import PLSCanonical >>> X = [[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [2.,5.,4.]] >>> Y = [[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]] >>> plsca = PLSCanonical(n_components=2) >>> plsca.fit(X, Y) PLSCanonical() >>> X_c, Y_c = plsca.transform(X, Y) r)rrRrTrr:r;)rqrrSrTrc Zt||dd||||dS)Nrr9rr)rrrqrrSrTrrs r6rzPLSCanonical.__init__sH %&  r8r rrrrr~rrrrrrrrs@r6rrs]]~$Cd&A#BDBBB+**""5))))              r8rceZdZUdZiejZeed<dD]Ze e d dddddfd Z xZ S) CCAat Canonical Correlation Analysis, also known as "Mode B" PLS. Read more in the :ref:`User Guide `. Parameters ---------- n_components : int, default=2 Number of components to keep. Should be in `[1, min(n_samples, n_features, n_targets)]`. scale : bool, default=True Whether to scale `X` and `Y`. max_iter : int, default=500 The maximum number of iterations of the power method. tol : float, default=1e-06 The tolerance used as convergence criteria in the power method: the algorithm stops whenever the squared norm of `u_i - u_{i-1}` is less than `tol`, where `u` corresponds to the left singular vector. copy : bool, default=True Whether to copy `X` and `Y` in fit before applying centering, and potentially scaling. If False, these operations will be done inplace, modifying both arrays. Attributes ---------- x_weights_ : ndarray of shape (n_features, n_components) The left singular vectors of the cross-covariance matrices of each iteration. y_weights_ : ndarray of shape (n_targets, n_components) The right singular vectors of the cross-covariance matrices of each iteration. x_loadings_ : ndarray of shape (n_features, n_components) The loadings of `X`. y_loadings_ : ndarray of shape (n_targets, n_components) The loadings of `Y`. x_rotations_ : ndarray of shape (n_features, n_components) The projection matrix used to transform `X`. y_rotations_ : ndarray of shape (n_features, n_components) The projection matrix used to transform `Y`. coef_ : ndarray of shape (n_targets, n_features) The coefficients of the linear model such that `Y` is approximated as `Y = X @ coef_.T + intercept_`. intercept_ : ndarray of shape (n_targets,) The intercepts of the linear model such that `Y` is approximated as `Y = X @ coef_.T + intercept_`. .. versionadded:: 1.1 n_iter_ : list of shape (n_components,) Number of iterations of the power method, for each component. n_features_in_ : int Number of features seen during :term:`fit`. feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- PLSCanonical : Partial Least Squares transformer and regressor. PLSSVD : Partial Least Square SVD. Examples -------- >>> from sklearn.cross_decomposition import CCA >>> X = [[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [3.,5.,4.]] >>> Y = [[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]] >>> cca = CCA(n_components=1) >>> cca.fit(X, Y) CCA(n_components=1) >>> X_c, Y_c = cca.transform(X, Y) rrrTr:r;rc Zt||ddd|||dS)NrrFrrrrs r6rz CCA.__init__hsH %&  r8rrrs@r6rr sUUn$Cd&A#BDBBB8**""5))))  '+cu4            r8rceZdZUdZeedddgdgdgdZeed<dd d d d Z e d dZ ddZ ddZ dS)raPartial Least Square SVD. This transformer simply performs a SVD on the cross-covariance matrix `X'Y`. It is able to project both the training data `X` and the targets `Y`. The training data `X` is projected on the left singular vectors, while the targets are projected on the right singular vectors. Read more in the :ref:`User Guide `. .. versionadded:: 0.8 Parameters ---------- n_components : int, default=2 The number of components to keep. Should be in `[1, min(n_samples, n_features, n_targets)]`. scale : bool, default=True Whether to scale `X` and `Y`. copy : bool, default=True Whether to copy `X` and `Y` in fit before applying centering, and potentially scaling. If `False`, these operations will be done inplace, modifying both arrays. Attributes ---------- x_weights_ : ndarray of shape (n_features, n_components) The left singular vectors of the SVD of the cross-covariance matrix. Used to project `X` in :meth:`transform`. y_weights_ : ndarray of (n_targets, n_components) The right singular vectors of the SVD of the cross-covariance matrix. Used to project `X` in :meth:`transform`. n_features_in_ : int Number of features seen during :term:`fit`. feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- PLSCanonical : Partial Least Squares transformer and regressor. CCA : Canonical Correlation Analysis. Examples -------- >>> import numpy as np >>> from sklearn.cross_decomposition import PLSSVD >>> X = np.array([[0., 0., 1.], ... [1., 0., 0.], ... [2., 2., 2.], ... [2., 5., 4.]]) >>> Y = np.array([[0.1, -0.2], ... [0.9, 1.1], ... [6.2, 5.9], ... [11.9, 12.3]]) >>> pls = PLSSVD(n_components=2).fit(X, Y) >>> X_c, Y_c = pls.transform(X, Y) >>> X_c.shape, Y_c.shape ((4, 2), (4, 2)) rGNrrrrrqrrrT)rqrc0||_||_||_dSr>r)rrrqrs r6rzPLSSVD.__init__s(  r8rct||||tj|jd}t |dtj|jd}|jdkr|dd}|j}t|j d|j d|j d}||krtd |d |d t|||j \}}|_|_|_|_tj|j|}t)|d \}}}|d d d |f}|d |}t+||\}}|j} ||_| |_|jj d|_|S)aJFit model to data. Parameters ---------- X : array-like of shape (n_samples, n_features) Training samples. Y : array-like of shape (n_samples,) or (n_samples, n_targets) Targets. Returns ------- self : object Fitted estimator. rrrQFrrGrrrrrrcN)rrr&rrrrrrrrMrrvrqrrrrr-rIrrrrr) rrPrQrrrdrer0rgVs r6rz PLSSVD.fits" 1%%%    RZdiA     #RZdi5    6Q;; "a  A ( qwqz171:qwqzBB * * *F1AFF#FFF  FV q$*F F B1dlDL$+t{ F13NNq...1b aaa,    B2 D#4Q7 r8ct|||tjd}||jz |jz }tj||j}|nt|ddtj}|j dkr| dd}||j z |j z }tj||j }||fS|S)a  Apply the dimensionality reduction. Parameters ---------- X : array-like of shape (n_samples, n_features) Samples to be transformed. Y : array-like of shape (n_samples,) or (n_samples, n_targets), default=None Targets. Returns ------- x_scores : array-like or tuple of array-like The transformed data `X_transformed` if `Y is not None`, `(X_transformed, Y_transformed)` otherwise. F)r#rNrQ)rrr#rGr)rrr&rrrr-rrrrrrr)rrPrQXrrYrrs r6rzPLSSVD.transforms&    5  A A$,$+ -6"do.. =A#bjQQQAv{{IIb!$$dl"dk1Bvb$/22HX% %r8cV|||||S)aLearn and apply the dimensionality reduction. Parameters ---------- X : array-like of shape (n_samples, n_features) Training samples. y : array-like of shape (n_samples,) or (n_samples, n_targets), default=None Targets. Returns ------- out : array-like or tuple of array-like The transformed data `X_transformed` if `Y is not None`, `(X_transformed, Y_transformed)` otherwise. rrs r6rzPLSSVD.fit_transformrr8rr>)rrrrrrrrrrrrrrrr8r6rrwsAAH"(AtFCCCD $$D 4 \55544654l@......r8r)r9r:r;Fr)0rrNabcrrnumbersrrnumpyr& scipy.linalgrbaser r r r r r exceptionsrutilsrrutils._param_validationrr utils.extmathr utils.fixesrrutils.validationrr__all__rrr7rarhrvr|r~rrrrrr8r6rs ''''''''"""""""",+++++88888888::::::::$$$$$$33333333<<<<<<<< 5 5 5u%%%%+******""""""<<<&=B8(8(8(8(v....4O9O9O9O9O9# O9O9O9O9d QQQQQDQQQh     4   Dh h h h h $h h h Vx.x.x.x.x. ,.> x.x.x.x.x.r8